\begin{abstract} 
  
  \noindent This paper compares three strategies in using reinforcement
  learning to have an artificial agent learn the game of Othello: Learning by
  self-play, learning from playing against a fixed opponent and learning from
  playing against a fixed opponent while paying attention to the opponent's
  moves as well. These issues are considered for the algorithms Q-learning,
  Sarsa and TD-learning. It is found that the best strategy of learning differs
  per algorithm. Additionally, the results indicate that the skill level of the
  training opponent is reflected in the eventual performance of the learning
  agent.  

\end{abstract}
